DSN\(^{\dag }\):
Our implementation of DSN is mathematically equivalent to the original [1]}, but is implemented differently. Our method calculates the closed-form solution to the regression objective, which is equivalent to calculating the projection of the query point into the subspace defined by the supports (for sufficiently small regularizer \(\lambda \) ; we use 0.01). [1]} instead calculates an orthonormal basis that spans the support points using SVD, and zeros out the components of the query point that fall into that subspace, yielding the displacement from the projection. While the underlying mechanics are different, the end result is the same.
